Publication

Book (*corresponding authors)

  1. L. Jiang* and C. Li. Bayesian Network Classifiers: Algorithms and Applications. China University of Geosciences Press, 2015. (ISBN 978-7-5625-3780-9)

Paper (*corresponding authors)

  1. L. Jiang*, G. Kong, and C. Li. Wrapper Framework for Test-Cost-Sensitive Feature Selection. IEEE Transactions on Systems Man Cybernetics-Systems, DOI: 10.1109/TSMC.2019.2904662.

  2. L. Yu, L. Jiang*, D. Wang, and L. Zhang. Toward Naive Bayes with Attribute Value Weighting. Neural Computing & Applications, DOI: 10.1007/s00521-018-3393-5.

  3. L. Zhang, L. Jiang*, and C. Li. A Discriminative Model Selection Approach and Its Application to Text Classification. Neural Computing & Applications, 2019, 31(4): 1173-1187.

  4. C. Li*, L. Jiang, and W. Xu. Noise Correction to Improve Data and Model Quality for Crowdsourcing. Engineering Applications of Artificial Intelligence, 2019, 82: 184-191.

  5. W. Xu, L. Jiang*, and L. Yu. An Attribute Value Frequency-based Instance Weighting Filter for Naive Bayes. Journal of Experimental & Theoretical Artificial Intelligence, 2019, 31(2): 225-236.

  6. L. Jiang* and C. Li. Two Improved Attribute Weighting Schemes for Value Difference Metric. Knowledge and Information Systems, 2019, 60(2): 949-970.

  7. L. Jiang*, L. Zhang, L. Yu, and D. Wang. Class-specific Attribute Weighted Naive Bayes. Pattern Recognition, 2019, 88: 321-330. [source code in WEKA 3.5.7]

  8. L. Jiang*, L. Zhang, C. Li, and J. Wu. A Correlation-based Feature Weighting Filter for Naive Bayes. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(2): 201-213. [source code in WEKA 3.7.1]

  9. H. Zhang, L. Jiang*, and W. Xu. Multiple Noisy Label Distribution Propagation for Crowdsourcing. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019, pp. 1473-1479.

  10. L. Yu, L. Jiang*, L. Zhang, and D. Wang. Weight Adjusted Naive Bayes. In: Proceedings of the 30th IEEE International Conference on Tools with Artificial Intelligence, 2018, pp. 825-831.

  11. C. Qiu, L. Jiang*, and Z. Cai. Using Differential Evolution to Estimate Labeler Quality for Crowdsourcing. In: Proceedings of the 15th Biennial Pacific Rim International Conference on Artificial Intelligence, LNAI 11013, 2018, pp. 165-173.

  12. H. Zhang, L. Jiang*, and W. Xu. Differential Evolution-Based Weighted Majority Voting for Crowdsourcing. In: Proceedings of the 15th Biennial Pacific Rim International Conference on Artificial Intelligence, LNAI 11013, 2018, pp. 228-236.

  13. L. Yu, L. Jiang*, D. Wang, and L. Zhang. Attribute Value Weighted Average of One-dependence Estimators. Entropy, 2017, 19(9): 501.

  14. C. Li, L. Jiang*, H. Li, J. Wu, and P. Zhang. Toward Value Difference Metric with Attribute Weighting. Knowledge and Information Systems, 2017, 50(3): 795-825.

  15. C. Qiu, L. Jiang*, and C. Li. Randomly Selected Decision Tree for Test-Cost Sensitive Learning. Applied Soft Computing, 2017, 53: 27-33.

  16. G. Kong, L. Jiang*, and C. Li*. Beyond Accuracy: Learning Selective Bayesian Classifiers with Minimal Test Cost. Pattern Recognition Letters, 2016, 80: 165-171.

  17. C. Li, S. Sheng, L. Jiang*, and H. Li*. Noise Filtering to Improve Data and Model Quality for Crowdsourcing. Knowledge-Based Systems, 2016, 107: 96-103.

  18. L. Zhang, L. Jiang*, C. Li*, and G. Kong. Two Feature Weighting Approaches for Naive Bayes Text Classifiers. Knowledge-Based Systems, 2016, 100: 137-144.

  19. L. Zhang, L. Jiang*, and C. Li. A New Feature Selection Approach to Naive Bayes Text Classifiers. International Journal of Pattern Recognition and Artificial Intelligence, 2016, 30(2): 1650003.

  20. L. Jiang*, C. Li*, S. Wang, and L. Zhang. Deep Feature Weighting for Naive Bayes and Its Application to Text Classification. Engineering Applications of Artificial Intelligence, 2016, 52: 26-39. [source code in WEKA 3.5.7]

  21. L. Jiang*, S. Wang, C. Li, and L. Zhang. Structure Extended Multinomial Naive Bayes. Information Sciences, 2016, 329: 346-356. [source code in WEKA 3.5.7]

  22. L. Zhang, L. Jiang*, and C. Li. C4.5 or Naive Bayes: A Discriminative Model Selection Approach. In: Proceedings of the 25th International Conference on Artificial Neural Networks, LNCS 9886, 2016, pp. 419-426.[Student Travel Award]

  23. C. Qiu, L. Jiang*, and C. Li. Not always simple classification: Learning SuperParent for Class Probability Estimation. Expert Systems with Applications, 2015, 42(13): 5433-5440.

  24. S. Wang, L. Jiang*, and C. Li. Adapting Naive Bayes Tree for Text Classification. Knowledge and Information Systems, 2015, 44(1): 77-89.

  25. L. Jiang*, C. Qiu, and C. Li. A Novel Minority Cloning Technique for Cost-Sensitive Learning. International Journal of Pattern Recognition and Artificial Intelligence, 2015, 29(4): 1551004.

  26. C. Qiu, L. Jiang*, and G. Kong. A Differential Evolution-Based Method for Class-Imbalanced Cost-Sensitive Learning. In: Proceedings of the 2015 International Joint Conference on Neural Networks, 2015, pp. 1-8.

  27. L. Jiang*, C. Li, and S. Wang. Cost-Sensitive Bayesian Network Classifiers. Pattern Recognition Letters, 2014, 45: 211-216.

  28. L. Jiang*, C. Li, H. Zhang, and Z. Cai. A Novel Distance Function: Frequency Difference Metric. International Journal of Pattern Recognition and Artificial Intelligence, 2014, 28(2): 1451002.

  29. L. Jiang*, Z. Cai, D. Wang, and H. Zhang. Bayesian Citation-KNN with Distance Weighting. International Journal of Machine Learning and Cybernetics, 2014, 5(2): 193-199.

  30. C. Li*, L. Jiang, and H. Li. Naive Bayes for Value Difference Metric. Frontiers of Computer Science, 2014, 8(2): 255-264.

  31. C. Li*, L. Jiang, and H. Li. Local Value Difference Metric. Pattern Recognition Letters, 2014, 49: 62-68.

  32. S. Wang, L. Jiang*, and C. Li. A CFS-based Feature Weighting Approach to Naive Bayes Text Classifiers. In: Proceedings of the 24th International Conference on Artificial Neural Networks, LNCS 8681, 2014, pp. 555-562.

  33. G. Li, O. Bräysy, L. Jiang, Z. Wu*, and Y. Wang. Finding Time Series Discord Based on Bit Representation Clustering. Knowledge-Based Systems, 2013, 54: 243-254.

  34. L. Jiang* and C. Li. An Augmented Value Difference Measure. Pattern Recognition Letters, 2013, 34(10): 1169-1174.

  35. L. Jiang, Z. Cai*, H. Zhang, and D. Wang. Naive Bayes Text Classifiers: A Locally Weighted Learning Approach. Journal of Experimental & Theoretical Artificial Intelligence, 2013, 25(2): 273-286.

  36. C. Li*, L. Jiang, H. Li, and S. Wang. Attribute Weighted Value Difference Metric. In: Proceedings of the 25th IEEE International Conference on Tools with Artificial Intelligence, 2013, pp. 575-580.

  37. L. Jiang*, C. Li, Z. Cai, and H. Zhang. Sampled Bayesian Network Classifiers for Class-Imbalance and Cost-Sensitive Learning. In: Proceedings of the 25th IEEE International Conference on Tools with Artificial Intelligence, 2013, pp. 512-517.

  38. L. Jiang*, Z. Cai*, H. Zhang, and D. Wang. Not so greedy: Randomly Selected Naive Bayes. Expert Systems with Applications, 2012, 39(12): 11022-11028.

  39. L. Jiang*, H. Zhang, Z. Cai, and D. Wang. Weighted Average of One-Dependence Estimators. Journal of Experimental & Theoretical Artificial Intelligence, 2012, 24(2): 219-230. [source code in WEKA 3.7.1]

  40. L. Jiang*, D. Wang, and Z. Cai. Discriminatively Weighted Naive Bayes and Its Application in Text Classification. International Journal on Artificial Intelligence Tools, 2012, 21(1): 1250007.

  41. L. Jiang, Z. Cai*, D. Wang, and H. Zhang. Improving Tree Augmented Naive Bayes for Class Probability Estimation. Knowledge-Based Systems, 2012, 26: 239-245.

  42. L. Jiang*. Learning Instance Weighted Naive Bayes from Labeled and Unlabeled Data. Journal of Intelligent Information Systems, 2012, 38(1): 257-268.

  43. L. Jiang* and C. Li. Scaling Up the Accuracy of Decision-Tree Classifiers: A Naive-Bayes Combination. Journal of Computers, 2011, 6(7): 1325-1331.

  44. L. Jiang* and C. Li. An Empirical Study on Class Probability Estimates in Decision Tree Learning. Journal of Software, 2011, 6(7): 1368-1373.

  45. L. Jiang*. Learning Random Forests for Ranking. Frontiers of Computer Science in China, 2011, 5(1): 79-86.

  46. L. Jiang*. Random One-Dependence Estimators. Pattern Recognition Letters, 2011, 32(3): 532-539.

  47. L. Jiang*, Z. Cai, and D. Wang. Improving Naive Bayes for Classification. International Journal of Computers and Applications, 2010, 32(3): 328-332.

  48. L. Jiang and C. Li*. An Empirical Study on Attribute Selection Measures in Decision Tree Learning. Journal of Computational Information Systems, 2010, 6(1): 105-112.

  49. L. Jiang*, H. Zhang, and Z. Cai. A Novel Bayes Model: Hidden Naive Bayes. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(10): 1361-1371. [source code in WEKA 3.7.1]

  50. L. Jiang*, C. Li, and Z. Cai. Learning Decision Tree for Ranking. Knowledge and Information Systems, 2009, 20(1): 123-135.

  51. L. Jiang*, C. Li, and Z. Cai. Decision Tree with Better Class Probability Estimation. International Journal of Pattern Recognition and Artificial Intelligence, 2009, 23(4): 745-763.

  52. L. Jiang*, D. Wang, Z. Cai, S. Jiang, and X. Yan. Scaling Up the Accuracy of K-Nearest-Neighbor Classifiers: A Naive-Bayes Hybrid. International Journal of Computers and Applications, 2009, 31(1): 36-43.

  53. L. Jiang*, Z. Cai, and D. Wang. Learning Averaged One-Dependence Estimators by Instance Weighting. Journal of Computational Information Systems, 2008, 4(6): 2753-2760.

  54. L. Jiang*, D. Wang, H. Zhang, Z. Cai, and B. Huang. Using Instance Cloning to Improve Naive Bayes for Ranking. International Journal of Pattern Recognition and Artificial Intelligence, 2008, 22(6): 1121-1140.

  55. W. Gong*, Z. Cai, and L. Jiang. Enhancing the Performance of Differential Evolution Using Orthogonal Design Method. Applied Mathematics and Computation, 2008, 206(1): 56-69.

  56. L. Jiang*, C. Li, J. Wu, and J. Zhu. A Combined Classification Algorithm Based on C4.5 and NB. In: Proceedings of the 3rd International Symposium on Intelligence Computation and Applications, LNCS 5370, 2008, pp. 350-359.

  57. L. Jiang*, H. Zhang, D. Wang, and Z. Cai. Learning Locally Weighted C4.4 for Class Probability Estimation. In: Proceedings of the 10th International Conference on Discovery Science, LNAI 4755, 2007, pp. 104-115.

  58. D. Wang* and L. Jiang. An Improved Attribute Selection Measure for Decision Tree Induction. In: Proceedings of the 4th International Conference on Fuzzy Systems and Knowledge Discovery, 2007, Volume 4, pp. 654-658.

  59. L. Jiang*, Z. Cai, D. Wang, and S. Jiang. Survey of Improving K-Nearest-Neighbor for Classification. In: Proceedings of the 4th International Conference on Fuzzy Systems and Knowledge Discovery, 2007, Volume 1, pp. 679-683.

  60. L. Jiang*, D. Wang, and Z. Cai. Scaling Up the Accuracy of Bayesian Network Classifiers by M-Estimate. In: Proceedings of the 3rd International Conference on Intelligent Computing, LNAI 4682, 2007, pp. 475-484.

  61. Z. Cai*, D. Wang, and L. Jiang. K-Distributions: A New Algorithm for Clustering Categorical Data. In: Proceedings of the 3rd International Conference on Intelligent Computing, LNAI 4682, 2007, pp. 436-443.

  62. L. Jiang*, D. Wang, Z. Cai, and X. Yan. Survey of Improving Naive Bayes for Classification. In: Proceedings of the 3rd International Conference on Advanced Data Mining and Applications, LNAI 4632, 2007, pp. 134-145.

  63. L. Jiang*, H. Zhang, and Z. Cai. Dynamic K-Nearest-Neighbor Naive Bayes with Attribute Weighted. In: Proceedings of the 3rd International Conference on Fuzzy Systems and Knowledge Discovery, LNAI 4223, 2006, pp. 365-368.

  64. L. Jiang* and H. Zhang. Weightily Averaged One-Dependence Estimators. In: Proceedings of the 9th Biennial Pacific Rim International Conference on Artificial Intelligence, LNAI 4099, 2006, pp. 970-974. [source code in WEKA 3.7.1]

  65. C. Li* and L. Jiang. Using Locally Weighted Learning to Improve SMOreg for Regression.In: Proceedings of the 9th Biennial Pacific Rim International Conference on Artificial Intelligence, LNAI 4099, 2006, pp. 375-384.

  66. L. Jiang* and H. Zhang. Lazy Averaged One-Dependence Estimators. In: Proceedings of the 19th Canadian Conference on Artificial Intelligence, LNAI 4013, 2006, pp. 515-525.

  67. L. Jiang* and H. Zhang. Learning Naive Bayes for Probability Estimation by Feature Selection. In: Proceedings of the 19th Canadian Conference on Artificial Intelligence, LNAI 4013, 2006, pp. 503-514.

  68. L. Jiang* and H. Zhang. Learning Instance Greedily Cloning Naive Bayes for Ranking. In: Proceedings of the 5th IEEE International Conference on Data Mining, 2005, pp. 202-209.

  69. L. Jiang* and Y. Guo. Learning Lazy Naive Bayesian Classifiers for Ranking. In: Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence, 2005, pp. 412-416.

  70. H. Zhang*, L. Jiang, and J. Su. Augmenting Naive Bayes for Ranking. In: Proceedings of the 22nd International Conference on Machine Learning, 2005, pp. 1020-1027.

  71. L. Jiang*, H. Zhang, and J. Su. Learning k-Nearest Neighbor Naive Bayes for Ranking. In: Proceedings of the 1st International Conference on Advanced Data Mining and Applications, LNAI 3584, 2005, pp. 175-185.

  72. L. Jiang*, H. Zhang, Z. Cai, and J. Su. One Dependence Augmented Naive Bayes. In: Proceedings of the 1st International Conference on Advanced Data Mining and Applications, LNAI 3584, 2005, pp. 186-194.

  73. H. Zhang*, L. Jiang, and J. Su. Hidden Naive Bayes. In: Proceedings of the 20th National Conference on Artificial Intelligence, 2005, pp. 919-924. [source code in WEKA 3.7.1]

  74. L. Jiang*, H. Zhang, and J. Su. Instance Cloning Local Naive Bayes. In: Proceedings of the 18th Canadian Conference on Artificial Intelligence, LNAI 3501, 2005, pp. 280-291.

  75. L. Jiang*, H. Zhang, Z. Cai, and J. Su. Learning Tree Augmented Naive Bayes for Ranking. In: Proceedings of the 10th International Conference on Database Systems for Advanced Applications, LNCS 3453, 2005, pp. 688-698.

  76. L. Jiang*, H. Zhang, Z. Cai, and J. Su. Evolutional Naive Bayes. In: Proceedings of the 1st International Symposium on Intelligence Computation and Applications, 2005, pp. 344-350.

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